Over the past three years, many new probabilistic tree-based (versus string-based) models have been designed and tested on many natural language applications, including MT. Most of these models turn out to be instances of tree transducers, a formal automata model first described by W. Rounds and J. Thatcher in 1970. These automata open up new opportunities for us to marry deeper representations, automata theory, and machine learning. This talk will cover new learning algorithms for tree automata, together with experiments in machine translation.
This is joint work with Jonathan Graehl (USC/ISI), Daniel Marcu (USC/ISI), and Dan Gildea (Rochester).